Digital economy in the UK: an evolutionary story


Emmanouil Tranos

University of Bristol, Alan Turing Institute
, @EmmanouilTranos, etranos.info

Contents


Introduction

Aims


  • Map the active engagement with the digital
  • Over time, early stages of the internet
  • Granular and multi-scale spatial perspective

Aims


  • Geographers used to be interested in diffusion

  • Hagerstrand et al. (1968)

  • Passed the torch to economists and sociologists

  • Why? Lack of granular data:

Because new digital activities are rarely—if ever—captured in official state data, researchers must rely on information gathered from alternative sources (Zook and McCanless 2022).

Importance

  • Understand how the adoption of new technologies evolves

  • Guide policies for deployment of new technologies

  • Predictions of introduction times for future technologies (Meade and Islam 2021):

    • Network operators

    • Suppliers of network equipment

    • Regulatory authorities

Technological diffusion


Spatial diffusion processes

  • As in temporal diffusion models, an S-shaped pattern in the cumulative level of adoption

  • A hierarchy effect: from main centres to secondary ones – central places

  • A neighborhood effect: diffusion proceeds outwards from innovation centres, first “hitting” nearby rather than far-away locations (Grubler 1990)

Hägerstrand (1965): from innovative centres (core) through a hierarchy of sub-centres, to the periphery

Web data

Long story short

  • Archived web data

  • Observe commercial websites 1996 - 2012

  • Geolocate to a unique location

  • Geolocate to multiple locations

Web data: The Internet Archive

  • The largest archive of webpages in the world
  • 273 billion webpages from over 361 million websites, 15 petabytes of storage (1996 -)
  • A web crawler starts with a list of URLs (a seed list) to crawl and downloads a copy of their content
  • Using the hyperlinks included in the crawled URLs, new URLs are identified and crawled (snowball sampling)
  • Time-stamp

Web data: The Internet Archive

Web data: The Internet Archive

Our web data

  • JISC UK Web Domain Dataset: all archived webpages from the .uk domain 1996-2012

  • Curated by the British Library

  • Tranos, E., and C. Stich. 2020. Individual internet usage and the availability of online content of local interest: A multilevel approach. Computers, Environment and Urban Systems, 79:101371.

  • Tranos, E., T. Kitsos, and R. Ortega-Argilés. 2021. Digital economy in the UK: Regional productivity effects of early adoption. Regional Studies, 55:12, 1924-1938.

  • Stich, C., E. Tranos and M. Nathan. 2022. Modelling clusters from the ground up: a web data approach. Environment and Planning B, in press.

  • Tranos, E., A. C. Incera and G. Willis. 2022. Using the web to predict regional trade flows: data extraction, modelling, and validation, Annals of the AAG, in press.

Our web data

  • All .uk archived webpages which contain a UK postcode in the web text

  • Circa 0.5 billion URLs with valid UK postcodes



20080509162138/http://www.website1.co.uk/contact_us IG8 8HD

Data cleaning

Unique postcodes frequencies, 2000

level freq perc cumfreq cumperc
(0,1] 41,596 0.718 41,596 0.718
(1,2] 6,451 0.111 48,047 0.830
(2,10] 6,163 0.106 54,210 0.936
(10,100] 2,975 0.051 57,185 0.988
(100,1000] 646 0.011 57,831 0.999
(1000,10000] 62 0.001 57,893 1.000
(10000,100000] 4 0.000 57,897 1.000


  • Websites with a large number of postcodes: e.g. directories, real estate websites

  • Focus on websites with one unique postcode per year

Directory website with a lot of postcodes

Website with a unique postcode in London

Web diffusion

Mapping website density

Spatial attributes

Neighborhood effect: diffusion proceeds outwards from innovation centers, first “hitting” nearby rather than far-away locations (Grubler 1990)

  • Spatial dependency (Moran’s I & LISA maps)

  • Website density regressions

  • Websites per firm in Local authorities (c. 400)

  • Websites in Output Areas (c. 200,000)

Website density regressions


\[Website\,Density_{i} = a + \beta Distance\,to\,Place_{i} + e_{i}\]


\(Website\,Density_{i}\):

  • Websites per firm in a Local Authority \(i\), or

  • Websites in an Output Area \(i\)

Website density regressions


\[Website\,Density_{i} = a + \beta Distance\,to\,Place_{i} + e_{i}\]


\(Place\):

  • London, or

  • Nearest city, or

  • Nearest retail centre

Website density regressions


\(\beta\) interpretation:

  • The lower the \(\beta\) is (or the larger the \(|\beta|\) is)…

  • … the larger urban gravitation is for web adoption.


Hierarchy effect: from main centers to secondary ones – central places

  • Gini coefficient

Spatial attributes, a summary

  • Spatial dependency relatively small and constant over time / scales

  • At local scale, consistent hotspots over time

  • More granular analysis reveals hotspots

  • Almost perfect polarisation of web adoption in the early stages at a granular level

  • More equally diffused at the Local Authority level

  • Plateau overtime

  • Distance effect: urban gravitation increases over time and then drops

  • Consistent across scales and definitions of urban

S-shaped diffusion curves

Rank dynamics: stability vs. deversity

to do: the same for OAs

Conclusions

What have we learned?

  • Geography matters: spatial dependency, urban gravitation

  • Some indications of a hierarchical diffusion

  • Granular analysis reveals patterns otherwise not visible

  • Well-established theoretical approaches of diffusion survive even at a granular level

What have we yet to learn?

  • Explain the spatial patterns of fast/slow web adoption

    • Applied the same analysis fo OA

    • Survival regressions (Perkins and Neumayer 2005)

  • Expand our definition of web to websites with # of postcodes > 1

References

Grubler, Arnulf. 1990. The Rise and Fall of Infrastructures: Dynamics of Evolution and Technological Change in Transport. Physica-Verlag.
Hagerstrand, Torsten et al. 1968. “Innovation Diffusion as a Spatial Process.” Innovation Diffusion as a Spatial Process.
Hägerstrand, Torsten. 1965. “A Monte Carlo Approach to Diffusion.” European Journal of Sociology/Archives Européennes de Sociologie 6 (1): 43–67.
Meade, Nigel, and Towhidul Islam. 2021. “Modelling and Forecasting National Introduction Times for Successive Generations of Mobile Telephony.” Telecommunications Policy 45 (3): 102088.
Perkins, Richard, and Eric Neumayer. 2005. “The International Diffusion of New Technologies: A Multitechnology Analysis of Latecomer Advantage and Global Economic Integration.” Annals of the Association of American Geographers 95 (4): 789–808.
Zook, Matthew, and Michael McCanless. 2022. “Mapping the Uneven Geographies of Digital Phenomena: The Case of Blockchain.” The Canadian Geographer/Le Géographe Canadien 66 (1): 23–36.